Efficient keyword spotting using time delay neural networks
@inproceedings{Myer2018EfficientKS, title={Efficient keyword spotting using time delay neural networks}, author={Samuel Myer and Vikrant Singh Tomar}, booktitle={INTERSPEECH}, year={2018} }
This paper describes a novel method of live keyword spotting using a two-stage time delay neural network. [] Key Method The model is trained using transfer learning: initial training with phone targets from a large speech corpus is followed by training with keyword targets from a smaller data set. The accuracy of the system is evaluated on two separate tasks. The first is the freely available Google Speech Commands dataset. The second is an in-house task specifically developed for keyword spotting. Theā¦
25 Citations
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A new network architecture (DenseNet-BiLSTM) is proposed for KWS, which removes the pool on the time dimension in transition layers to preserve speech time series information and outperforms the state-of-the-art methods in terms of accuracy on Google Speech Commands dataset.
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- Computer ScienceICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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DonUT is presented, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection and has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud.
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